{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 折线图、柱状图、箱图\n",
    "\n",
    "欢迎大家回到 Machine Learning Diary👏\n",
    "\n",
    "\n",
    "今天要做的事情\n",
    "\n",
    "1. 理解数据 - titanic\n",
    "2. 理解数据的基础上，玩数据\n",
    "\n",
    "\n",
    "## 一、Titanic练习赛介绍\n",
    "\n",
    "Titanic练习赛主要就是预测乘客是否存活，训练集中有乘客的若干特征与存活情况，乘客特征是年龄，性别等信息。使用训练集训练出一个模型，然后利用该模型去预测测试集中乘客的存活情况。\n",
    "\n",
    "## 二、特征分析与选择\n",
    "\n",
    "训练集中乘客的特征有：PassengerId，Pclass，Name，Sex，Age，SibSp，Parch，Ticket，Fare，Cabin和Embarked。\n",
    "\n",
    "接下来我们就对几个特征（变量）进行数据分析和可视化分析。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一般会在一开始把要用到的package都import进来。如果运行下面这个框时遇到 ImportError: No module named 'xxx'，就去pip install下来，如果忘了怎么下载package，课程链接：[魔法口令 - “pip install xxx”](https://github.com/YZHANG1270/Girls-In-AI/blob/master/machine_learning_diary/package/import.md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "\n",
    "# pandas\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "# numpy, matplotlib, seaborn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的代码框里首先用pandas把数据读进来，这节课我们先来分析train.csv这个数据文件。以及，pd.read_csv(\"D:/kaggle/titanic/data/train.csv\")里面的路径记得换成你自己的，ok？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get titanic & test csv files as a DataFrame\n",
    "titanic_df = pd.read_csv(\"D:/kaggle/titanic/data/train.csv\")\n",
    "\n",
    "# preview the data\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# 观察数据 和 数据类型\n",
    "titanic_df.info()\n",
    "# print(\"----------------------------\")\n",
    "# test_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "尝试删除几个你觉得没有用的变量，比如 ['PassengerId','Name','Ticket']，使用df.drop()就可以。删除之后可以用titanic_df.info()再检查是否已删除这些字段。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop unnecessary columns, these columns won't be useful in analysis and prediction\n",
    "titanic_df = titanic_df.drop(['PassengerId','Name','Ticket'], axis=1)\n",
    "# test_df    = test_df.drop(['Name','Ticket'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      "Survived    891 non-null int64\n",
      "Pclass      891 non-null int64\n",
      "Sex         891 non-null object\n",
      "Age         714 non-null float64\n",
      "SibSp       891 non-null int64\n",
      "Parch       891 non-null int64\n",
      "Fare        891 non-null float64\n",
      "Cabin       204 non-null object\n",
      "Embarked    889 non-null object\n",
      "dtypes: float64(2), int64(4), object(3)\n",
      "memory usage: 62.7+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embarked\n",
    "\n",
    "接下来研究下Embarked这个变量数据，Port of Embarkation，登船城市，C Q S 分别代表不同的城市。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x15709731c88>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Embarked\n",
    "\n",
    "# only in titanic_df, fill the two missing values with the most occurred value, which is \"S\".\n",
    "# 将缺失值用出现频率最高的 S 代替\n",
    "titanic_df[\"Embarked\"] = titanic_df[\"Embarked\"].fillna(\"S\")\n",
    "\n",
    "# plot\n",
    "# 画出存活率和Embarked之间的折线图，统计频率\n",
    "sns.factorplot('Embarked','Survived', data=titanic_df,size=4,aspect=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来画一个有3个子图的柱状图：\n",
    "1. 将Embarked统计频次\n",
    "2. 按照是否survived将3种Embarked统计频次\n",
    "3. 在survived里统计三种Embarked的频率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1570aeee208>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axis1,axis2,axis3) = plt.subplots(1,3,figsize=(15,5))\n",
    "\n",
    "\n",
    "# 1. 将Embarked统计频次\n",
    "sns.countplot(x='Embarked', data=titanic_df, ax=axis1)\n",
    "\n",
    "# 2. 按照是否survived将3种Embarked统计频次\n",
    "sns.countplot(x='Survived', hue=\"Embarked\", data=titanic_df, order=[1,0], ax=axis2)\n",
    "\n",
    "# 3. 在survived里统计三种Embarked的频率\n",
    "# group by embarked, and get the mean for survived passengers for each value in Embarked\n",
    "embark_perc = titanic_df[[\"Embarked\", \"Survived\"]].groupby(['Embarked'],as_index=False).mean()\n",
    "\n",
    "sns.barplot(x='Embarked', y='Survived', data=embark_perc,order=['S','C','Q'],ax=axis3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fare\n",
    "\n",
    "接下来研究下 Fare 这个变量数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert from float to int\n",
    "# 画图的过程中要注意各种数据类型是否为数字本身\n",
    "titanic_df['Fare'] = titanic_df['Fare'].astype(int)\n",
    "\n",
    "# get fare for survived & didn't survive passengers \n",
    "fare_not_survived = titanic_df[\"Fare\"][titanic_df[\"Survived\"] == 0]\n",
    "fare_survived     = titanic_df[\"Fare\"][titanic_df[\"Survived\"] == 1]\n",
    "\n",
    "# get average and std for fare of survived/not survived passengers\n",
    "avgerage_fare = DataFrame([fare_not_survived.mean(), fare_survived.mean()])\n",
    "std_fare      = DataFrame([fare_not_survived.std(), fare_survived.std()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画Fare相关的柱状图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15709731080>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3oAAAC9CAYAAADlVg/pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAE+RJREFUeJzt3X+QXXV5x/H3LoEgMwtFi1ZtIAPK4zpO1WwhFMGkUqWY0qhVx6FQC3Uy7cQZMrUjKMFESzvqACOKPxjCD6UyYyWEliIlU39ABDT1GlsY7zwoEbEyMCHTmAhd6ZLtH/ekXpINOfvj3Hty7vs1k+Gcc8+5+2zm2Us+e77n+x2anJxEkiRJktQcw/0uQJIkSZI0twx6kiRJktQwBj1JkiRJahiDniRJkiQ1jEFPkiRJkhpmXr8LOJBWq+W0oJIkSZIG2tjY2NB0zq990AMYGxvrdwnSlNrtNqOjo/0uQ9qHvak6sz9VV/am6qrVak37GoduSpIkSVLDGPQkSZIkqWEqGboZEYcA1wIBPAucDxwF3A78qDjt85n5lYhYAywDJoBVmbm5ipokSZIkaVBU9Yze2QCZ+YaIWApcSSfkXZmZV+w5KSIWAUuAxcACYD1wUkU1SZIkSdJAqGToZmbeBqwodo8DngDGgGURcU9EXBcRI8BpwMbMnMzMR4F5EXFMFTVJkiRJ0qCobNbNzJyIiC8CbwfeCbwcWJeZrYi4BFgD7AC2d122i84Qz23d79Vut6sqU5qV8fFx+1O1ZG+qzuxP1ZW9qSapdHmFzHxvRFwEfBc4NTN/Xry0AfgM8E/ASNclI3TC33M4ze3cW3jxHf0uoZEe+fiyfpcgAU4RrnqzP1VX9qbqqjbLK0TEeRHxoWL3aWA3cGtEnFwcOwNoAfcCZ0bEcEQcCwxn5pNV1CRJkiRJg6KqO3q3AjdExD3AocAq4GfA1RHxDPA4sCIzd0bEJuB+OqFzZUX1SJIkSdLAqCToZeZTwLuneOnUKc5dC6ytog5JkiRJGkQumC5JkiRJDWPQkyRJkqSGMehJkiRJUsMY9CRJkiSpYQx6kiRJktQwBj1JkiRJahiDniRJkiQ1jEFPkiRJkhrGoCdJkiRJDWPQkyRJkqSGMehJkiRJUsMY9CRJkiSpYQx6kiRJktQwBj1JkiRJahiDniRJkiQ1zLwq3jQiDgGuBQJ4FjgfGAJuBCaBB4GVmbk7ItYAy4AJYFVmbq6iJkmSJEkaFFXd0TsbIDPfAHwEuLL4szozT6cT+pZHxCJgCbAYeA/w2YrqkSRJkqSBUckdvcy8LSL+pdg9DniCzl27u4tjdwJvARLYmJmTwKMRMS8ijsnMbd3v1263qyhTmnP2qupifHzcflRt2Z+qK3tTTVJJ0APIzImI+CLwduCdwB8VgQ5gF3AUcCSwveuyPcefE/RGR0erKnOAbe13AY1kr6ou2u22/ajasj9VV/am6qrVak37mkonY8nM9wIn0nle7wVdL40AO4CdxfbexyVJkiRJM1RJ0IuI8yLiQ8Xu08Bu4HsRsbQ4dhawCbgXODMihiPiWGA4M5+soiZJkiRJGhRVDd28FbghIu4BDgVWAW3g2og4rNi+JTOfjYhNwP10QufKiuqRJEmSpIFR1WQsTwHvnuKlJVOcuxZYW0UdkiRJkjSIXDBdkiRJkhrGoCdJkiRJDWPQkyRJkqSGMehJkiRJUsMY9CRJkiSpYQx6kiRJktQwBj1JkiRJahiDniRJkiQ1jEFPkiRJkhrGoCdJkiRJDWPQkyRJkqSGMehJkiRJUsMY9CRJkiSpYQx6kiRJktQw8+b6DSPiUOB6YCEwH7gM+C/gduBHxWmfz8yvRMQaYBkwAazKzM1zXY8kSZIkDZo5D3rAucD2zDwvIl4EbAE+BlyZmVfsOSkiFgFLgMXAAmA9cFIF9UiSJEnSQKki6H0VuKVrfwIYAyIiltO5q7cKOA3YmJmTwKMRMS8ijsnMbXu/YbvdrqBMae7Zq6qL8fFx+1G1ZX+qruxNNcmcB73M/CVARIzQCXyr6QzhXJeZrYi4BFgD7AC2d126CzgK2CfojY6OznWZYmu/C2gke1V10W637UfVlv2purI3VVetVmva11QyGUtELAC+CdyUmTcDGzJzT3UbgNcDO4GRrstG6IQ/SZIkSdIszHnQi4iXABuBizLz+uLwXRFxcrF9BtAC7gXOjIjhiDgWGM7MJ+e6HkmSJEkaNFU8o/dh4Gjg0oi4tDj218CnIuIZ4HFgRWbujIhNwP10AufKCmqRJEmSpIFTxTN6FwIXTvHSqVOcuxZYO9c1SJIkSdIgc8F0SZIkSWoYg54kSZIkNYxBT5IkSZIaplTQK2bSlCRJkiQdBMpOxrI+IrYB1wFfy8zdFdYkSZIkSZqFUnf0MvM0OssmLAHui4i/i4jjK61MkiRJkjQj03lG7zFgK/A08Brgqoj4WCVVSZIkSZJmrOwzev9IZ2Hzo4FzM3N5Zp4NvLXK4iRJkiRJ01f2jt61wMmZ+ffAZNfx0+a+JEmSJEnSbJQNeqcCHy22Px0RFwNk5nglVUmSJEmSZqxs0PvjzPwAQGa+Czi7upIkSZIkSbNRNujtjojDACLi0GlcJ0mSJEnqsbLr6H0BeDAiHgBeBXyyupIkSZIkSbNRKuhl5nUR8c/A8cDDmflktWVJkiRJkmaqVNCLiNcBK4DDi30y84L9nHsocD2wEJgPXAb8ELiRzoydDwIrM3N3RKwBlgETwKrM3Dybb0aSJEmSVH7o5o3A1cDPSpx7LrA9M8+LiBcBW4AfAKsz81sR8QVgeUT8FFgCLAYWAOuBk6ZZvyRJkiRpL2WD3uOZua7kuV8FbunanwDGgLuL/TuBtwAJbMzMSeDRiJgXEcdk5raSX0eSJEmSNIWyQe+RYu28LRQLpmfmxqlOzMxfAkTECJ3Atxq4vAh0ALuAo4Ajge1dl+45vk/Qa7fbJcuU+steVV2Mj4/bj6ot+1N1ZW+qScoGvflAFH+gE/amDHoAEbEA2AB8LjNvjojuWTpHgB3AzmJ77+P7GB0dLVmmytva7wIayV5VXbTbbftRtWV/qq7sTdVVq9Wa9jVlZ908PyJOBE4AHgAe29+5EfESOiHw/Zn59eLwlohYmpnfAs4Cvgn8GPhkRFwO/DYw7GyekiRJkjR7ZWfdfD/wduCFdCZmeSXw/v2c/mHgaODSiLi0OHYh8Oli0fU2cEtmPhsRm4D76SzAvnKm34QkSZIk6dfKDt18D3A68I3MvCoi/n1/J2bmhXSC3d6WTHHuWmBtyRokSZIkSSUMT/O8PROq/KqCWiRJkiRJc6DsHb2bgXuA4yLia8Bt1ZUkSZIkSZqNspOxXB0RXwde09nN/6y2LEmSJEnSTJUauhkRHwHeBYwCbyv2JUmSJEk1VHbo5hPFf4eARZR/tk+SJEmS1GNlh25e070fEXdWU44kSZIkabbKrqN3YtfuS4FjqylHkiRJkjRbZYdudt/RGwf+poJaJEmSJElzoOzQzd+vuhBJkiRJ0twoO3TzP4AROnfzDi8ODwGTmXl8RbVJkiRJkmag7OyZ9wF/mpmvBpYD3wZeRWe5BUmSJElSjZR9Ru/VmXk/QGY+EBHHZuavKqxLkiRJkjRDZYPejoj4W2AzcBrw0+pKkiRJkiTNRtmhm+cAO4E/BLYCf1FZRZIkSZKkWSkb9MaB/waeBBL4jcoqkiRJkiTNynTW0XsMeDPwPeBLwFuf74KIWAx8IjOXRsQi4HbgR8XLn8/Mr0TEGmAZMAGsyszNM/geJEmSJEldyga9EzLzfRFxembeHhEXP9/JEfFB4DzgqeLQIuDKzLyi65xFwBJgMbAAWA+cNN1vQJIkSZL0XGWD3ryI+E1gMiJGgN0HOP9h4B3ATcX+GBARsZzOXb1VdCZ12ZiZk8CjETEvIo7JzG17v1m73S5ZptRf9qrqYnx83H5Ubdmfqit7U01SNuhdAtwLvBT4DnDh852cmesjYmHXoc3AusxsRcQlwBpgB7C965xdwFHAPkFvdNTl+ube1n4X0Ej2quqi3W7bj6ot+1N1ZW+qrlqt1rSvKTsZy4LMDOAE4DWZ+W/T/DobMnNPdRuA19OZxXOk65wROuFPkiRJkjQLZYPeCoDM3FYMtZyuuyLi5GL7DKBF5w7hmRExHBHHAsOZ+eQM3luSJEmS1KXs0M35EbGFztIKuwEy85xpfJ2/Aq6OiGeAx4EVmbkzIjYB99MJnCun8X6SJEmSpP143qAXEasz8zLgIuDlwM/LvnFmPgKcUmx/Hzh1inPWAmtLVytJkiRJOqAD3dF7E3BZZt4dEd/IzDf1oihJkiRJ0swd6Bm9of1sS5IkSZJq6kBBb3I/25IkSZKkmjrQ0M2xiLiPzt28V3dtT2bmPs/cSZIkSZL670BB73d6UoUkSZIkac48b9DLzJ/2qhBJkiRJ0twou2C6JEmSJOkgYdCTJEmSpIY50DN6kqZh4cV39LuExnnk48v6XYIkSdJBxzt6kiRJktQwBj1JkiRJahiDniRJkiQ1jEFPkiRJkhrGoCdJkiRJDVPZrJsRsRj4RGYujYhXADcCk8CDwMrM3B0Ra4BlwASwKjM3V1WPJEmSJA2KSu7oRcQHgXXA4cWhK4HVmXk6MAQsj4hFwBJgMfAe4LNV1CJJkiRJg6aqO3oPA+8Abir2x4C7i+07gbcACWzMzEng0YiYFxHHZOa2imqSJKlSrqU5HVtLn+l6mpI0fZUEvcxcHxELuw4NFYEOYBdwFHAksL3rnD3H9wl67Xa7ijIlHQT8+Z+Z8fFx/+7UGPayesXPTjVJZc/o7WV31/YIsAPYWWzvfXwfo6Oj1VU2sMr/JlXqJ3/+Z6bdbvt31xd+tlbBXlav+Nmpumq1WtO+plezbm6JiKXF9lnAJuBe4MyIGI6IY4HhzHyyR/VIkiRJUmP16o7eB4BrI+IwoA3ckpnPRsQm4H46gXNlj2qRJEmSpEarLOhl5iPAKcX2Q3Rm2Nz7nLXA2qpqkCRJkqRB5ILpkiRJktQwBj1JkiRJahiDniRJkiQ1jEFPkiRJkhrGoCdJkiRJDWPQkyRJkqSGMehJkiRJUsMY9CRJkiSpYQx6kiRJktQwBj1JkiRJahiDniRJkiQ1jEFPkiRJkhrGoCdJkiRJDWPQkyRJkqSGmdfLLxYRW4BfFLs/Aa4BrgImgI2Z+dFe1iNJkiRJTdSzoBcRhwNk5tKuYz8A/gTYCtwREYsy8/u9qkmSJEmSmqiXd/ReCxwRERuLr7sWmJ+ZDwNExF3AGYBBT5IkSZJmoZdB72ngcmAd8ErgTmBH1+u7gOOnurDdbldenKR68ud/ZsbHx/27U2PYy+oVPzvVJL0Meg8BP87MSeChiPgF8MKu10d4bvD7f6Ojoz0ob9Bs7XcBUin+/M9Mu932764v/Gytgr2sXvGzU3XVarWmfU0vZ928ALgCICJeBhwBPBURJ0TEEHAmsKmH9UiSJElSI/Xyjt51wI0R8W1gkk7w2w18GTiEzqyb3+1hPZIkSZLUSD0Lepn5DHDOFC+d0qsaJB18Fl58R79LOIg5jFCSpEHlgumSJEmS1DAGPUmSJElqGIOeJEmSJDVMLydjkSRJmjaf1Z17j3x8Wb9LkFQx7+hJkiRJUsMY9CRJkiSpYQx6kiRJktQwBj1JkiRJahiDniRJkiQ1jLNuSpIkDRhnMn0+W2d8pbOZqk4OiqDnh5EkSZIklefQTUmSJElqGIOeJEmSJDXMQTF0U5IkSao7HzdSVda/67emfY139CRJkiSpYfp+Ry8ihoHPAa8FfgW8LzN/3N+qJEmSJOngVYc7em8DDs/M3wMuBq7ocz2SJEmSdFDr+x094DTgXwEy8zsR8bt7nzCTMamSJEmSNKjqEPSOBH7Rtf9sRMzLzAmAsbGxof6UJUmSJEkHpzoM3dwJjHTtD+8JeZIkSZKk6atD0LsXeCtARJwCPNDfciRJkiTp4FaHoZsbgDdHxH3AEHB+n+uRJEmSpIPa0OTkZL9rmJLLLqiOImIx8InMXBoRrwBuBCaBB4GVmbm7n/VpMEXEocD1wEJgPnAZ8EPsT/VZRBwCXAsE8CydX+YOYW+qJiLixUALeDMwgb2pmoiILfx6HpOfANcAV9Hp042Z+dEDvUcdhm7uj8suqFYi4oPAOuDw4tCVwOrMPJ3OP1yW96s2Dbxzge1FL54FXI39qXo4GyAz3wB8hE5f2puqheKXZNcA/1McsjdVCxFxOEBmLi3+nA98ATiHzooFiyNi0YHep85B7znLLgD7LLsg9djDwDu69seAu4vtO4E/6HlFUsdXgUu79iewP1UDmXkbsKLYPQ54AntT9XE5nX88P1bs25uqi9cCR0TExoj4RkS8EZifmQ9n5iRwF3DGgd6kzkFvymUX+lWMlJnrgf/tOjRU/LAB7AKO6n1VEmTmLzNzV0SMALcAq7E/VROZORERXwQ+Q6c/7U31XUT8ObAtM+/qOmxvqi6epvOLiDOBvwRuKI7tUao/6xz0XHZBddc9bn8E2NGvQqSIWAB8E7gpM2/G/lSNZOZ7gRPpPK/3gq6X7E31ywV0JgP8FvA64EvAi7tetzfVTw8B/5CZk5n5EJ2bXy/ser1Uf9Y56LnsgupuS0QsLbbPAjb1sRYNsIh4CbARuCgzry8O25/qu4g4LyI+VOw+TecXEN+zN9VvmfnGzFySmUuBHwB/Btxpb6omLqCYnyQiXgYcATwVESdExBCdO30H7M86D4V02QXV3QeAayPiMKBNZ0iS1A8fBo4GLo2IPc/qXQh82v5Un90K3BAR9wCHAqvo9KOfnaoj/7+uurgOuDEivk1nFtgL6Pyi7MvAIXRm3fzugd6ktssrSJIkSZJmps5DNyVJkiRJM2DQkyRJkqSGMehJkiRJUsMY9CRJkiSpYQx6kiRJktQwBj1JkiRJahiDniRJkiQ1zP8BgS2YmmxQSosAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "# 1 画出Fare的频次统计\n",
    "titanic_df['Fare'].plot(kind='hist', figsize=(15,3),bins=100, xlim=(0,50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1570b0ef0f0>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u7wTewCB8v5iZow+pfBZ4VUQ8EzgxM/cDz2Uw4r9luO/TgF8G/i0zH8zMnwC3tf5DSkdhiKu65zEYEa8drt/N4InWA8DPDdueP7L97MjyzcCvABcxGHn/n8w8BHSBqxi8nwbgLuCqzNwMvJpB0N8LPCcifioijhv2Jy0ZQ1ylZeZu4Bbg2xFxK3AT8A7gT4GrI+Im4LgF9p0DrgfWZOY9YzbZCWwB/m64fiXw6uFI/EZgf2bez2Cu/BvAHqDfzk8mTcYnNiWpMEfiklSYIS5JhRniklSYIS5JhRniklSYIS5JhRniklSYIS5Jhf0vfK3EYdhxXK0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2 画出根据是否survived的Fare的箱图\n",
    "avgerage_fare.index.names = std_fare.index.names = [\"Survived\"]\n",
    "avgerage_fare.plot(yerr=std_fare,kind='bar',legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Age\n",
    "\n",
    "接下来研究下 Age 这个变量数据。首先画一个年纪的分布图，再对年龄做数据处理，重新画图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Yi\\Anaconda3\\envs\\python35\\lib\\site-packages\\ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1570b161a58>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 首先确定有连个子图\n",
    "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))\n",
    "\n",
    "# 图1 的 title\n",
    "axis1.set_title('Original Age values - Titanic')\n",
    "# 图2 的 title\n",
    "axis2.set_title('New Age values - Titanic')\n",
    "\n",
    "# 用原始年龄数据画图\n",
    "# plot original Age values\n",
    "# NOTE: drop all null values, and convert to int\n",
    "titanic_df['Age'].dropna().astype(int).hist(bins=70, ax=axis1)\n",
    "\n",
    "# 对年龄进行数据处理\n",
    "# fill NaN values in Age column with random values generated\n",
    "# 用随机数据填充确实年龄信息\n",
    "titanic_df[\"Age\"][np.isnan(titanic_df[\"Age\"])] = rand_1\n",
    "\n",
    "# convert from float to int\n",
    "# 对数据进行 整数处理\n",
    "titanic_df['Age'] = titanic_df['Age'].astype(int)\n",
    "        \n",
    "# plot new Age Values\n",
    "# 画新图\n",
    "titanic_df['Age'].hist(bins=70, ax=axis2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1570b135c18>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 911x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# .... continue with plot Age column\n",
    "\n",
    "# 继续画一张曲线图，按照survived分开画\n",
    "# peaks for survived/not survived passengers by their age\n",
    "facet = sns.FacetGrid(titanic_df, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, titanic_df['Age'].max()))\n",
    "facet.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1570af2ba58>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据年龄统计存活的平均人数\n",
    "# average survived passengers by age\n",
    "fig, axis1 = plt.subplots(1,1,figsize=(18,4))\n",
    "average_age = titanic_df[[\"Age\", \"Survived\"]].groupby(['Age'],as_index=False).mean()\n",
    "sns.barplot(x='Age', y='Survived', data=average_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cabin\n",
    "\n",
    "接下来研究下 Cabin 这个变量数据，但是缺失值太多，不好做展示，这次不做仔细分析。直接删去。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cabin\n",
    "# It has a lot of NaN values, so it won't cause a remarkable impact on prediction\n",
    "titanic_df.drop(\"Cabin\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Family\n",
    "\n",
    "接下来研究下 Family 这个变量数据，看看存活率跟是否一家出行有多大的关系。这个Family的变量是根据Parch & SibSp这两个变量统计出来的。如果Parch & SibSp不为0，代表乘客是和家里人一起出行的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Yi\\Anaconda3\\envs\\python35\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "# Family\n",
    "\n",
    "# Instead of having two columns Parch & SibSp, \n",
    "# we can have only one column represent if the passenger had any family member aboard or not,\n",
    "# Meaning, if having any family member(whether parent, brother, ...etc) will increase chances of Survival or not.\n",
    "titanic_df['Family'] =  titanic_df[\"Parch\"] + titanic_df[\"SibSp\"]\n",
    "titanic_df['Family'].loc[titanic_df['Family'] > 0] = 1\n",
    "titanic_df['Family'].loc[titanic_df['Family'] == 0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'With Family'), Text(0,0,'Alone')]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "fig, (axis1,axis2) = plt.subplots(1,2,sharex=True,figsize=(10,5))\n",
    "\n",
    "# sns.factorplot('Family',data=titanic_df,kind='count',ax=axis1)\n",
    "sns.countplot(x='Family', data=titanic_df, order=[1,0], ax=axis1)\n",
    "\n",
    "# average of survived for those who had/didn't have any family member\n",
    "family_perc = titanic_df[[\"Family\", \"Survived\"]].groupby(['Family'],as_index=False).mean()\n",
    "sns.barplot(x='Family', y='Survived', data=family_perc, order=[1,0], ax=axis2)\n",
    "\n",
    "axis1.set_xticklabels([\"With Family\",\"Alone\"], rotation=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sex\n",
    "\n",
    "接下来研究下 Sex 这个变量数据，看看存活率跟性别有多大的关系。首先按照是否满16岁，将人群分为是否为\"成人\"，再分男人和女人。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sex\n",
    "\n",
    "# As we see, children(age < ~16) on aboard seem to have a high chances for Survival.\n",
    "# So, we can classify passengers as males, females, and child\n",
    "def get_person(passenger):\n",
    "    age,sex = passenger\n",
    "    return 'child' if age < 16 else sex\n",
    "    \n",
    "titanic_df['Person'] = titanic_df[['Age','Sex']].apply(get_person,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dummy variables for Person column, & drop Male as it has the lowest average of survived passengers\n",
    "person_dummies_titanic  = pd.get_dummies(titanic_df['Person'])\n",
    "person_dummies_titanic.columns = ['Child','Female','Male']\n",
    "person_dummies_titanic.drop(['Male'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画两张图：\n",
    "1. 按性别、成人统计人数\n",
    "2. 按性别和成人与否统计存活率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1570b48bf60>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_df = titanic_df.join(person_dummies_titanic)\n",
    "\n",
    "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(10,5))\n",
    "\n",
    "# sns.factorplot('Person',data=titanic_df,kind='count',ax=axis1)\n",
    "sns.countplot(x='Person', data=titanic_df, ax=axis1)\n",
    "\n",
    "# average of survived for each Person(male, female, or child)\n",
    "person_perc = titanic_df[[\"Person\", \"Survived\"]].groupby(['Person'],as_index=False).mean()\n",
    "sns.barplot(x='Person', y='Survived', data=person_perc, ax=axis2, order=['male','female','child'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pclass\n",
    "\n",
    "接下来研究下 Pclass 这个变量数据，画个存活率的统计概率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1570b4a69e8>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pclass\n",
    "\n",
    "# sns.factorplot('Pclass',data=titanic_df,kind='count',order=[1,2,3])\n",
    "sns.factorplot('Pclass','Survived',order=[1,2,3], data=titanic_df,size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, 现在有没有对数据的可视化稍微有点感觉啦？ 下次我们还会分析更多有趣的数据。\n",
    "\n",
    "👩好啦，今天的课程就到这里啦！咱们下次见！~"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
